Multi-label Learning Algorithm of Regression Kernel Extreme Learning Machine
WANG Yibin1,2 , CHENG Yusheng1,2, HE Yue1, PEI Gensheng1
1.School of Computer and Information, Anqing Normal University, Anqing 246133 2.University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246133
Abstract In the multi-label learning algorithms based on extreme learning machine(ELM), the ELM classification model is often used, and the correlation between labels is ignored. Accordingly, a multi-label learning algorithm of regression kernel extreme learning machine with association rules(ML-ASRKELM) is proposed in this paper. Firstly, the rule vectors between labels are extracted by analyzing the association rules of label space. Then, the prediction results are obtained by the proposed multi-label regression kernel extreme learning machine(ML-RKELM). Eventually, if the rule vectors are not empty, the final results are calculated by the rule vectors and the prediction results of ML-KRELM. Otherwise, the final results are predicted by ML-RKELM. The experimental results show that ML-ASRKELM and ML-RKELM are superior to other algorithms, and the effectiveness of the proposed algorithms are illustrated by the statistical hypothesis test.
Corresponding Authors:
CHENG Yusheng, Ph.D., professor. His research interests include big data, rough sets and machine learning for feature selection.
About author:: WANG Yibin, master, professor. His research interests include multi-label learning, machine learning and software security HE Yue, master student. Her research interests include multi-label learning, machine learning and big data. PEI Gensheng, master student. His research interests include machine learning, big data and data statistics.
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2018, Vol. 31 
